ArticleDiagnostics (Basel, Switzerland)2022
Application of a Deep Learning System in Pterygium Grading and Further Prediction of Recurrence with Slit Lamp Photographs.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Artificial Intelligence for Diagnostic Guidance in Ocular Surface Disorders.Journal of clinical medicine · 2026Review
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Artificial intelligence derived grading of mustard gas induced corneal injury and opacity.Scientific reports · 2025Article
- A semantic segmentation-based automatic pterygium assessment and grading system.Frontiers in medicine · 2025Article
- Transfer Learning and Multi-Feature Fusion-Based Deep Learning Model for Idiopathic Macular Hole Diagnosis and Grading from Optical Coherence Tomography Images.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Unravelling the molecular tapestry of pterygium: insights into genes for diagnostic and therapeutic innovations.Eye (London, England) · 2024Review
- A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images.Diagnostics (Basel, Switzerland) · 2024Article
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
- Intelligent diagnostic model for pterygium by combining attention mechanism and MobileNetV2.International journal of ophthalmology · 2024Article
- Artificial intelligence-assisted diagnosis of ocular surface diseases.Frontiers in cell and developmental biology · 2023Review
- DBPF-net: dual-branch structural feature extraction reinforcement network for ocular surface disease image classification.Frontiers in medicine · 2023Article
- Artificial intelligence assisted pterygium diagnosis: current status and perspectives.International journal of ophthalmology · 2023Article
- Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2022Review
- Advances in artificial intelligence applications for ocular surface diseases diagnosis.Frontiers in cell and developmental biology · 2022Review
- Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images.Frontiers in neuroscience · 2022Article
- A Generalized and Interpretable Multi-Label Multi-Disease Screening System for Ocular Anterior Segment Disease Detection.Ophthalmology scienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundThe aim of this study was to evaluate the efficacy of a deep learning system in pterygium grading and recurrence prediction.
methodsThis was a single center, retrospective study. Slit-lamp photographs, from patients with or without pterygium, were collected to develop an algorithm. Demographic data, including age, gender, laterality, grading, and pterygium area, recurrence, and surgical methods were recorded. Complex ocular surface diseases and pseudopterygium were excluded. Performance of the algorithm was evaluated by sensitivity, specificity, F1 score, accuracy, and area under the receiver operating characteristic curve. Confusion matrices and heatmaps were created to help explain the results.
resultsA total of 237 eyes were enrolled, of which 176 eyes had pterygium and 61 were non-pterygium eyes. The training set and testing set were comprised of 189 and 48 photographs, respectively. In pterygium grading, sensitivity, specificity, F1 score, and accuracy were 80% to 91.67%, 91.67% to 100%, 81.82% to 94.34%, and 86.67% to 91.67%, respectively. In the prediction model, our results showed sensitivity, specificity, positive predictive value, and negative predictive values were 66.67%, 81.82%, 33.33%, and 94.74%, respectively.
conclusionsDeep learning systems can be useful in pterygium grading based on slit lamp photographs. When clinical parameters involved in the prediction of pterygium recurrence were included, the algorithm showed higher specificity and negative predictive value in prediction.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.